Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling

Jakob Prange; Nathan Schneider; Lingpeng Kong

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arXiv · 2112.07874

Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling

Jakob Prange·Nathan Schneider·Lingpeng Kong

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Abstract

We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different formalisms, we find that, overall, semantic constituency structures are most useful to language modeling performance -- outpacing syntactic constituency structures as well as syntactic and semantic dependency structures. Further, effects vary greatly depending on part-of-speech class. In sum, our findings point to promising tendencies in neuro-symbolic language modeling and invite future research quantifying the design choices made by different formalisms.

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Jakob Prange, Nathan Schneider, Lingpeng Kong. 2021-12-15. Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling. https://arxiv.org/abs/2112.07874

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